Theoretical Rationale For Neurolearning SPC Dyslexia Screening Test App

The most commonly used definitions of dyslexia in the English-speaking world^[i],[ii]agree in two key respects:

• That dyslexia’s core diagnostic feature is difficulty developing fast and accurate reading and spelling skills (particularly at the level of sounding out and spelling words) which are unexpected in relation to an individual’s intelligence, age, and education.
• That challenges with the phonological component of language (essentially the way the brain identifies and processes sounds in words) are a key component of the brain-based differences underlying dyslexic reading and spelling challenges.^[iii] (See Shaywitz, S. Overcoming Dyslexia for a good review of the phonological aspects of dyslexia.)

The Rose Review, which is used in the UK, also notes that challenges with verbal memory and processing speed have also been shown to play a role in dyslexic learning challenges. (See ref. 2)

Both definitions were based on a comprehensive review of the research on dyslexia available at the time of its formulation, and each reflected the broad consensus of understanding current among dyslexia researchers at the time of its formulation.^[iv]

As additional research has been accumulated, several important additional points about dyslexia have been recognized:

• Dyslexia has increasingly been recognized as a syndrome that is multifactorial in origin, having many possible underlying cognitive contributors rather than just one (i.e., phonological impairments). These factors include not only verbal memory and processing speed (as per the Rose Review), but also word retrieval and naming speed^[v], visual attention^[vi], procedural learning and memory^[vii],and overall language ability. (See ref. 4)
• Consistent with the growing recognition of this multifactorial origin, it has been increasingly recognized that the overall likelihood that an individual will show dyslexic reading and spelling challenges rises with the number of risk factors that the individual possesses.^[viii] As a result, dyslexia risk assessment requires a clear and well-defined way of integrating the results from a wide variety of assessments of different cognitive skills.
• It has also become increasingly clear that students with high verbal ability may experience highly significant challenges with reading and spelling (especially with speed or fluency of reading, stamina for reading, ability to decode unfamiliar words, spelling and writing) that are clearly dyslexic in nature, despite scoring at or above population means on tests of phonological awareness, reading comprehension, and/or other traditional dyslexia assessments.^[ix] (Verbal IQ, as measured on the WISC, has a correlation of approximately 80% with Vocabulary^[x], as measured both on the WISC and our app, so our app uses Vocabulary as a stand-in for Verbal IQ.) For such students, proper identification depends the recognition of discrepancies between their own personal areas of strength and weakness, rather than on comparisons of their weaknesses with population norms.^[xi] For example, a very bright third grade student who tests into a gifted program with a verbal IQ of 135 (99^th percentile), and who tests at the 80^th percentile on an untimed reading comprehension test (i.e., reading short stories or informational paragraphs) but who also tests at the 50^th percentile on tests of phonological awareness and single word decoding and spelling, will very likely also show academically significant difficulties keeping up class work in reading and writing, and will show difficulties spelling, making “silly mistakes” in reading test items, etc., that are dyslexic in nature and require special intervention, even though by most traditional measures they don’t qualify for a diagnosis of dyslexia. Similarly, among the age 5-6 pre-reading population, individual discrepancies between verbal ability and word and sub-word level reading skills can already be seen, and these often provide the only tip-off that the child is at high risk of dyslexia-related reading and spelling challenges. No other assessment currently in use identifies these children.
• In addition, our series of more than 150 consecutive dyslexic students examined in our own clinic has revealed patterns of relationships between scores on different cognitive measures that are highly distinctive and robust for dyslexic students, and highly predictive both of academic challenges and potentially successful interventions^[xii]. We’ve appended a graph to this document [see figure below] showing how these students scored on WISC-IV IQ and WIAT-III achievement tests. This graph reveals the characteristic relationships between scores on the various subtests that dyslexic students show, and which are almost entirely preserved across the range of IQs. Note, for example, the consistent broad gap between relatively higher verbal and non-verbal comprehension scores on WISC IQ, and the relatively lower working memory and processing speed scores. Also note the across-the-board reductions in academic fluency skills in such areas as math calculation, reading rate, and essay production. These internal relationships are highly consistent at the individual as well as the population level, and form another signature for dyslexic processing that we have found to have value both in diagnosis, and in recommending interventions.
• Finally, we have found that an individual student’s variations from these characteristic patterns have predictive value in deciding upon what form of interventions they’ll be most likely to benefit from. For example, in the areas of reading and spelling, there are many different kinds of interventions available, some of which use muscle memory, others speech-related memory, others training of auditory processing functions, others visualization or verbal memory (mnemonics), etc. Based upon a student’s unique combination of results in the kinds of subtests featured in our app, we can guide instructors toward the use of interventions that will be especially likely to work successfully with an individual student (and away from less successful methods). The specificity with which we can deliver these results also means that our highly individualized recommendations will fit quite in instructional settings using UDL concepts and methods. [These patterns will be used to make recommendations, but will mostly not be part of the risk assessment algorithm]